Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551039
Pertenecen a esta colección Tesis y Trabajos de grado de las Maestrías correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
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- In-process quality 4.0 control reference framework and methodology for CNC machine tools in tmart manufacturing(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025-06-02) Ramírez Doñé, Samuel José; Romero Díaz, David C.; emimmayorquin; Wuest, Thorsten; Rodríguez González, Ciro A.; Sánchez Olvera, Raúl; School of Engineering and Sciences; Campus Monterrey; Kurfess, ThomasThe Industry 4.0 paradigm has changed the shop floor by digitalising and smartifying operations through technology to achieve superior product quality and production performance. This ongoing trend has granted manufacturers varying technological tools and methods to improve their products and processes. Quality 4.0 has also surfaced as the application of Industry 4.0 technologies for Quality Management purposes to leverage its unique advantages for further quality operations enhancement. In this context, In-Process Quality 4.0 Controls (IPQ4.0Cs) have emerged as a “predictivepreventive” approach for ensuring part quality by controlling critical parts and process elements during manufacturing. However, unlike the predominant but inefficient post-process quality control practices, the adoption of IPQ4.0Cs remains limited even in traditional CNC machine tools manufacturing environments. Considering IPQ4.0Cs’ efficient, robust, and autonomous approach to supporting high-quality manufacturing operations, a need for further research was established in this Master’s thesis work. After a careful state-of-the-art review of IPQ4.0Cs in the scientific literature, it was demonstrated that there is a lack of standardised guidelines for their adoption in the industry, challenging Quality anagers to implement them in their manufacturing operations. Thus, this thesis aims to address this challenge by proposing an “In-Process Quality 4.0 Control Reference Framework and Methodology for CNC Machine Tools in Smart Manufacturing” that is useful, practical, and understandable to support Quality Managers in adopting In-Process Quality 4.0 Controls in their CNC machine tools.
- An autonomic lift truck for a smart factory(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-05-10) Marín Segura, Juan Daniel; Carrillo, Luis Antonio; emimmayorquin; Torres, David Antonio; Reyes Avendaño, Jorge Antonio; Camacho León, Sergio; School of Engineering and Sciences; Campus Puebla; Hernández Zarate, Debbie CrystalRapid technological advancements have introduced the concept of Smart Factory (SF), which requires systems with high levels of flexibility, adaptability, and digitization. This the- sis explores the design and implementation of an autonomic Lift Truck to achieve these char- acteristics within a SF environment. The proposed system uses the MAPE-K (Monitoring, Analysis, Planning, Execution, and Knowledge) framework to create an autonomic system (AcS), which is a self-managing system that includes four characteristics: self-configuration, self-healing, self-optimization, and self-protection (Self-CHOP). The transformation of a Turtlebot3 into an autonomic lift truck, encompassing mechan- ical modifications, electrical system enhancements, and software integration is addressed in this research thesis. Also, the interaction of the forklift with the SF, focusing on its ability to map and adjust to layout changes (self-configuration), send alerts for human intervention during faults (self-healing), optimize energy usage based on demand (self-optimization), and prevent hardware and software failures (self-protection) are tackled out. Pilot tests demonstrate the effectiveness of the autonomic Lift Truck in bringing flexi- bility, adaptability, and digitization to the SF. Results indicate that the proposed system can dynamically adapt to changes in a factory layout, maintain operational continuity through self-healing mechanisms, optimize resource usage based on real-time demands, and protect operations against potential disruptions. Summarizing, this thesis contributes to the field of AcS by presenting one of the first implementations that integrates all the features of Self-CHOP within an Industry 4.0 context. It provides a fundamental framework for future research and development of advanced AcS into smart manufacturing environments
- Development and implementation of IOT-based systems for monitoring and digitalization in pursuit of a smart manufacturing approach(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024) Moreno Arias, Mario Ramiro; Urbina Coronado, Pedro Daniel; mtyahinojosa, emipsanchez; Güemes Castorena, David; Orta Castañón, Pedro Antonio; Escuela de Ingeniería y Ciencias; Campus Monterrey; Ahuett Garza, HoracioIndustry 4.0 has introduced a new way of using technology within manufacturing processes, generating new opportunities for optimization and efficiency. This digital transformation allows companies to increase productivity through automation and system integration and facilitates better decision-making based on accurate, real-time data and metrics. Furthermore, embracing this new technological era allows customized production and ongoing process improvement. Currently, many solutions based on cutting-edge technologies are costly, complex to implement in legacy systems, and lack compatibility in conventional manufacturing environments, making it difficult for SMEs to adopt such systems and compete with larger companies with more complex systems. Implementing innovative and Industry 4.0-based technologies does not necessarily have to be expensive and inflexible. This project seeks to design and integrate low-cost systems that can be implemented in conventional work environments for real-time environmental variable monitoring outside machines. It also aims to implement sensors and digitization processes for legacy machines to give them a second life and enhance performance. To achieve this, a balance must be found between the components used and their ability to accomplish the task, making it essential to understand how to integrate them. Thus, the proposed methodology involves using simple yet powerful sensors, adaptable programming, low-cost microcontrollers capable of connecting and sending information to the cloud, and real-time data visualization tools. This work gathered information from various SMEs about current opportunities and needs and how our systems can facilitate change. The outcome of the systems development provides a comprehensive pair of solutions that offer capabilities for real-time monitoring, digitization, trend analysis and information, physical test validation, and the implementation of cloud solutions.